Several of our students and faculty will be participating in the 2015 AGU fall meeting. Research topics include spatial variability of urban floods, spatio-temporal variability and tele-connections of the large basin floods, demand induced drought risk tools for the continental USA, innovative forecasting techniques for climate induced agricultural risk indicators and scaling of extreme rainfall at a planetary scale among others. The following topics will be presented.


NG22A-01:  Scaling of Extreme Rainfall Areas at a Planetary Scale (Naresh Devineni, Upmanu Lall, Chen Xi and Philip Ward)

Event magnitude and area scaling relationships for rainfall over different regions of the world, have been presented in the literature for relatively short durations and over relatively small areas. In this paper, we present the first ever results on a global analysis of the scaling characteristics of extreme rainfall areas for durations ranging from 1 to 30 days. Broken power law models are fit in each case. The past work has been focused largely on the time and space scales associated with local and regional convection. The work presented here suggests that power law scaling may also apply to planetary scale phenomenon, such as frontal and monsoonal systems, and their interaction with local moisture recycling. Such features may have persistence over large areas corresponding to extreme rain and regional flood events. As a result they lead to considerable hazard exposure. A caveat is that methods used for empirical power law identification have difficulties with edge effects due to finite domains. This leads to problems with robust model identification and interpretability of the underlying relationships. We use recent algorithms that aim to address some of these issues in a principled way. Theoretical research that could explain why such results may emerge across the world, as analyzed for the first time in this paper, is needed.

NH52B-02:  An integrated statistical – physical modeling approach for multivariate flood risk assessment (Naresh Devineni and Tara Troy)
Hydrologic models require spatio-temporal weather data as forcing, and maintaining spatio-temporal dependence across these variables is important in stochastic simulations. The dependence across the weather variables can be nonlinear, and each variable may follow a different probability model. The dimension of the problem can be relatively large to be represented through a parametric modeling framework. We introduce a new space-time simulator for multiple variables and spatial locations and demonstrate its application for modeling floods in two different river basins with different climatologies. Given multiple variables, each of which can be simulated from its marginal or conditional distribution, and a historical data set for these variables, the method appropriately preserves multivariate and the temporal dependence across the variables. We use the VIC hydrologic model, which is physically based, to show the necessity of preserving both the spatial and temporal patterns in flood modeling. We demonstrate that the spatial structure is not needed for small basin areas, but as one moves to larger river basins, it becomes increasingly important. This research bridges the divide between stochastic weather simulators and flood models, demonstrating that merging the two approaches can produce more robust estimates of flood distributions and return period estimation.
NH51F-1958:  Classifying Intensity and Area of Extreme Rainfall Events in Greater New York Area Using Weather Radar Data (Ali Hamidi, Naresh Devineni, James Booth, Reza Khanbilvardi and Ralph Ferarro)
Extreme rainfall events, specifically in urban areas, have dramatic impacts on society and can lead to loss of lives and properties. Despite these hazards, little is known about the city-scale variability of heavy rain events. In the current study, 13 years of gridded Stage IV radar data, 2002-2014, is employed to investigate the statistical properties of the spatiotemporal variability of simultaneous rainfall exceedances in Greater New York Area. The 95th percentile of each gridpoint’s annual max intensity is considered as a threshold for storms. Then, multivariate k-means clustering is applied on extreme rainfall events’ intensity and area exposure for each rainfall duration and season of occurrence. Comparison of timing indicates most of extreme rainfall events (more than 40%) are occurring in summer. Clustering analysis results show that for short rainfall duration, most of the study area is hit by high intensity-large area storm in warm seasons while in cold seasons rainfall intensity is low and the areal exposure is also low. In contrast, long rainfall duration follows an opposite spatiotemporal pattern. Resultant maps geo-reference the probability of occurrence of high-intensity large-area exposure storm over the study area. These maps can become inputs for design of hydraulic systems with the spatial and temporal resolution of 4km X 4km and 1-hour respectively which corresponds to the input radar data.
H21O-07:  Development of a Demand Sensitive Drought Index and its Application for Agriculture over the Conterminous United States (Elius Etienne, Naresh Devineni, Reza Khanbilvardi and Upmanu Lall)
A new drought index is introduced that explicitly considers both water supply and demand. It can be applied to aggregate demand over a geographical region, or for disaggregated demand related to a specific crop or use. Consequently, it is more directly related than existing indices, to potential drought impacts on different segments of society, and is also suitable to use as an index for drought insurance programs targeted at farmers growing specific crops. An application of the index is presented for the drought characterization at the county level for the aggregate demand of eight major field crops in the conterminous United States. Two resiliency metrics are developed and applied with the drought index time series. In addition, a clustering algorithm is applied to the onset times and severity of the worst historical droughts in each county, to identify the spatial structure of drought, relative to the cropping patterns in each county. The geographic relationship of drought severity, drought recovery relative to duration, and resilience to drought is identified, and related to attributes of precipitation and also cropping intensity, thus distinguishing the relative importance of water supply and demand in determining potential drought outcomes.

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